Sequential decision analysis techniques for e-sports

ABSTRACT

Aspects of the subject disclosure may include, for example, training a decision scoring model for an e-sport based on historical data for the e-sport. In various embodiments, the decision scoring model may be trained based on the historical data and on metadata associated with the e-sport. Some embodiments can include identifying a plurality of candidate in-game decision sequences based on decision parameters for a gameplay decision of an ongoing gaming session and a gaming session history for the ongoing gaming session. Various embodiments can include applying the decision scoring model to rank the plurality of candidate in-game decision sequences. Other embodiments are disclosed.

FIELD OF THE DISCLOSURE

The subject disclosure relates to sequential decision analysistechniques for electronic sports (e-sports).

BACKGROUND

Electronic sports (e-sports) has developed into a billion-dollarindustry in the past few years, and is expected to grow more in the nearfuture. Such sports are often based on video games that are multiplayerand real-time strategy based. During the course of such competitivegames, split-second decisions are constantly made in the hopes ofachieving victory. Analysis of the gameplay, either real time bycommentators or post-hoc by players/teams/analysts often involvesreactions and commentary to “gut feelings” and decisions perceived to becorrect and incorrect.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIG. 2 is a block diagram illustrating an example, non-limitingembodiment of a first operating environment in accordance with variousaspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a decision analysis scheme.

FIG. 4 is a block diagram illustrating an example, non-limitingembodiment of a second operating environment in accordance with variousaspects described herein.

FIG. 5 depicts an illustrative embodiment of a method in accordance withvarious aspects described herein.

FIG. 6 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 7 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 8 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 9 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for evaluating gameplay decisions in e-sports gameplaysessions. According to techniques described herein, the effects ofpresent-time choices upon the nature of gameplay decisions that may beexpected to arise in the future are taken into account. Some embodimentscan include training a decision scoring model for an e-sport based onhistorical data for the e-sport. In various such embodiments, thedecision scoring model may be trained based on the historical data andon metadata associated with the e-sport. Some embodiments can includeidentifying a plurality of candidate in-game decision sequences based ondecision parameters for a gameplay decision of an ongoing gaming sessionand a gaming session history for the ongoing gaming session. Variousembodiments can include applying the decision scoring model to rank theplurality of candidate in-game decision sequences. Other embodiments aredescribed in the subject disclosure.

One or more aspects of the subject disclosure include an apparatuscomprising a processing system including a processor and a memory thatstores executable instructions that, when executed by the processingsystem, facilitate performance of operations. The operations can includetraining a decision scoring model for an e-sport based on historicaldata for the e-sport, determining decision parameters for a gameplaydecision of an ongoing gaming session of the e-sport, identifying aplurality of candidate in-game decision sequences based on the decisionparameters and a gaming session history for the ongoing gaming session,and applying the decision scoring model to rank the plurality ofcandidate in-game decision sequences.

One or more aspects of the subject disclosure include a non-transitorymachine-readable medium, comprising executable instructions that, whenexecuted by a processing system including a processor, facilitateperformance of operations. The operations can include training adecision scoring model for an e-sport based on historical data for thee-sport, determining decision parameters for a gameplay decision of anongoing gaming session of the e-sport, identifying a plurality ofcandidate in-game decision sequences based on the decision parametersand a gaming session history for the ongoing gaming session, andapplying the decision scoring model to rank the plurality of candidatein-game decision sequences.

One or more aspects of the subject disclosure include a method. Themethod can include training a decision scoring model for an e-sportbased on historical data for the e-sport, determining decisionparameters for a gameplay decision of an ongoing gaming session of thee-sport, identifying a plurality of candidate in-game decision sequencesbased on the decision parameters and a gaming session history for theongoing gaming session, and applying the decision scoring model to rankthe plurality of candidate in-game decision sequences.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a system 100 in accordance withvarious aspects described herein. For example, system 100 can facilitatein whole or in part training a decision scoring model for an e-sportbased on historical data for the e-sport, determining decisionparameters for a gameplay decision of an ongoing gaming session of thee-sport, identifying a plurality of candidate in-game decision sequencesbased on the decision parameters and a gaming session history for theongoing gaming session, and applying the decision scoring model to rankthe plurality of candidate in-game decision sequences. In particular, acommunications network 125 is presented for providing broadband access110 to a plurality of data terminals 114 via access terminal 112,wireless access 120 to a plurality of mobile devices 124 and vehicle 126via base station or access point 122, voice access 130 to a plurality oftelephony devices 134, via switching device 132 and/or media access 140to a plurality of audio/video display devices 144 via media terminal142. In addition, communication network 125 is coupled to one or morecontent sources 175 of audio, video, graphics, text and/or other media.While broadband access 110, wireless access 120, voice access 130 andmedia access 140 are shown separately, one or more of these forms ofaccess can be combined to provide multiple access services to a singleclient device (e.g., mobile devices 124 can receive media content viamedia terminal 142, data terminal 114 can be provided voice access viaswitching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIG. 2 is a block diagram illustrating a non-limiting example of anoperating environment 200 that may be representative of variousembodiments. In operating environment 200, a plurality of players 202-1to 202-N compete in an e-sport by participating in a gaming session ofthe e-sport. According to some embodiments, teams may be formed fromsome or all of players 202-1 to 202-N, and those teams may competeagainst each other during the gaming session. In the example depicted inFIG. 2, a team 203 is formed, featuring players 202-1, 202-2, and 202-3as members. In various embodiments, participants in the gaming sessionmay compete against each both individually and as teams. In some otherembodiments, participants in the gaming session may compete asindividuals only. In still other embodiments, some participants in thegaming session may compete as individuals only, while other participantsmay compete as members of teams, in addition to or in lieu of competingindividually. The embodiments are not limited in this context.

202-1 to 202-N participate in the gaming session using respectivee-sport client devices 204-1 to 204-N. Each of e-sport client devices204-1 to 204-N may comprise respective processing circuitry to run ane-sport client application that enables participation in the gamingsession of the e-sport. Any or all of e-sport client devices 204-1 to204-N may comprise additional components facilitating participation inthe gaming session. For example, in various embodiments, any or all ofe-sport client devices 204-1 to 204-N can additionally include one ormore input devices to receive player input during the gaming session,one or more display devices to display visual effects during the gamingsession, and/or one or more audio devices to generate audio effectsduring the gaming session. The embodiments are not limited to thisexample.

In some embodiments, an e-sport coordinator 240 can coordinate thee-sport gaming session. As reflected in FIG. 2, in various embodiments,e-sport coordinator 240 can comprise an e-sport coordinator applicationrunning on an e-sport server 230. During the e-sport gaming session,e-sport coordinator 240 can communicate, via a communication network205, with e-sport client applications running on e-sport client devices204-1 to 204-N. In a given embodiment, for any given one of e-sportclient devices 204-1 to 204-N, connectivity to e-sport coordinator 240via communication network 205 can constitute wired connectivity,wireless connectivity, or a combination of both.

Participants in the e-sport gaming session in operating environment 200may be faced with various types of gameplay decisions at various pointsin time over the course of the session. In the case of many e-sports, arapid pace of play may afford participants little time to consider theiroptions when handling such gameplay decisions. As a result, it may bedifficult for participants, as well as commentators and spectators, toassess the extent to which participants' decision-making has aided orhindered their efforts to win. This difficulty may be compounded by thefact that different available options for a present decision may giverise to different future gameplay decisions.

FIG. 3 is a block diagram illustrating a non-limiting example of adecision analysis scheme 300 that may be implemented in some embodimentsin order to provide e-sports participants and observers with improvedinsight into gameplay decisions. According to decision analysis scheme300, a decision scoring model is used to evaluate the available optionsfor gameplay decisions, while a decision simulator is used to identifyfuture gameplay decisions that may arise from selection of such options.The decision scoring model may generally accept inputs comprising listsof options for gameplay decisions of the e-sport, evaluate the optionsin such lists, and rank the options in order of preference, based on theevaluations. Optionally, the decision scoring model may determinepreference scores for the options.

The decision scoring model can be trained using historical data for therelevant e-sport. The historical data can include statistics generallycharacterizing results of past gaming sessions of the e-sport. Thesestatistics can include statistics aggregated at the player level,statistics aggregated at the team level, or both. Examples of suchstatistics can include, without limitation, win/loss records,gold/experience point accrual totals/rates, and kill counts/rates. Thehistorical data can also include player histories generallycharacterizing aspects of the performance of given players in pastgaming sessions. Such player histories can include statistics such asthose discussed above, broken down in terms of heroes/roles played(attack/support/defend), time (evolution of skill level), past teams, acombination of these, and/or other factors. The historical data canadditionally include game information associated with the e-sport inquestion. Such game information can comprise, for example, update logsfrom developers of the e-sport, and can describe changes that haveoccurred over time with respect to maps, game mechanics, and/orheroes/characters of the e-sport.

Optionally, the decision scoring model can be trained using metadataassociated with the e-sport. Such metadata can include game timelineinformation describing timings of events, such as fights, kills, anditem buys, that occur during gaming sessions of the e-sport. Suchmetadata can additionally/alternatively include player positioningtimeline information describing the positions of players in gamingsessions of the e-sport, continuously or at discrete points in time.Such metadata can additionally/alternatively include map informationdescribing various aspects, such as size, topography, barriers, and/orplayer line of sight, of maps associated with gaming sessions of thee-sport. Optionally, metadata extraction model(s) may be used to obtainsome or all of such metadata by analyzing video and/or audio feeds ofgaming sessions of the e-sport.

In some embodiments, the historical data (and optionally, metadata) usedto train the decision scoring model may be restricted to a subset ofgaming sessions (e.g., only gaming sessions featuring given teams asparticipants) and/or particular time period(s) (e.g., only gamingsessions within the preceding two years). In some embodiments,differential weighting may be applied across time and/or player segmentsin conjunction with training the decision scoring model. In someembodiments, missing parts of the feature space may be inferred usingmathematical smoothing techniques such as interpolation or imputation.

When used to evaluate options for gameplay decisions during an ongoinge-sport gaming session, the decision scoring model may accept inputsincluding a game history for the ongoing gaming session, a decision typecharacterizing the gameplay decision, and a list of options to beconsidered for the gameplay decision. The game history may compriseinformation generally describing results/aspects of the ongoing gamingsession through a time t generally corresponding to the present.Examples of decision types can include, without limitation, heropick/ban decisions, item purchase decisions, gold expenditure decisions,in-game team formation decisions, and attack/defend decisions.

During an initial iteration, the decision scoring model may be used torank/score the available options for the gameplay decision underconsideration. A check may then be performed to determine whether atermination condition has been satisfied. In some embodiments, thetermination condition may require decision scoring at a number of timepoints that are pre-supplied instead of being generated by the decisionsimulator. In some embodiments, the termination condition may impose alower bound on decision scores, such that the evaluation processcontinues until a threshold score is exceeded. In some embodiments, thetermination condition may impose an upper bound on the lengths ofdecision sequences to be considered using the decision scoring model.

If the termination condition has not been satisfied following a giveniteration, a subsequent iteration or “loop” may be initiated. During thesubsequent iteration/loop, the decision simulator may be provided withfeedback regarding the results returned by the decision scoring modelduring the preceding iteration/loop. Based on such feedback, thedecision simulator may predict future gaming decisions, and availableoptions for such future gaming decisions, that may arise as a result ofvarious possible choices associated with gaming decisions at earlierpoints in time. The decision simulator may output a list of decisionsequences, which may then be accepted as input, evaluated, andranked/scored using the decision scoring model.

FIG. 4 is a block diagram illustrating a non-limiting example of anoperating environment 400 that may be representative of variousembodiments. In operating environment 400, an e-sport analyzer 420generally performs decision analysis in accordance with decisionanalysis scheme 300 of FIG. 3. As shown in FIG. 4, e-sport analyzer 420comprises a decision evaluator 422, a decision simulator 424, and ametadata extractor 426. Decision evaluator 422 generallyanalyzes/evaluates gameplay decisions of an e-sport using the decisionscoring model of decision analysis scheme 300. Decision simulator 424generally performs operations corresponding to those of the decisionsimulator of decision analysis scheme 300. Metadata extractor 426generally extracts metadata 427 from video feed(s) 416 and/or audiofeed(s) 417 using metadata extraction model(s) such as may optionally beused in conjunction with decision analysis scheme 300.

An e-sport reporter 410 communicates with e-sport coordinator 240 toobtain various types of information associated with e-sport gamingsessions coordinated by e-sport coordinator 240. Such information caninclude any or all of historical data 412, metadata 414, video feed(s)416, audio feed(s) 417, and session information 418. Historical data 412can include statistics generally characterizing results of past gamingsessions of the e-sport, and generally corresponds to the historicaldata used to train the decision scoring model of decision analysisscheme 300 of FIG. 3. Metadata 414 (and/or metadata 427) can includegame timeline information describing timings of events, such as fights,kills, and item buys, that occur during gaming sessions of the e-sport,player positioning timeline information describing the positions ofplayers in gaming sessions of the e-sport, and/or map informationdescribing various aspects, such as size, topography, barriers, and/orplayer line of sight, of maps associated with gaming sessions of thee-sport. Video feed(s) 416 and audio feed(s) 417 generally comprisevideo/audio feeds of gaming sessions of the e-sport. Session information418 generally comprises information describing aspects of an ongoinggaming session of the e-sport. Session information 418 can include agaming session history for the ongoing gaming session, which may serveas input to the decision scoring model of decision analysis scheme 300.

E-sport analyzer 420 may train the decision scoring model based onhistorical data 412, metadata 414, and/or metadata 427. E-sport analyzer420 may then use the decision scoring model to evaluate gameplaydecisions during the ongoing gaming session. In conjunction withevaluating such gameplay decisions during the ongoing gaming session,e-sport analyzer 420 may identify candidate decision sequences 429 basedon decision parameters 428 and the gaming session history for theongoing gaming session, and may apply the decision scoring model to rankcandidate decision sequences 429. Decision parameters 428 may generallycomprise information characterizing a gameplay decision of the ongoinggaming session. Decision parameters 428 can include informationidentifying a decision type characterizing the gameplay decision,information indicating a timing of the gameplay decision, and a list ofoptions to be considered for the gameplay decision.

FIG. 5 depicts an illustrative embodiment of a method 500 in accordancewith various aspects described herein. According to some embodiments,method 500 may be representative of the implementation of decisionanalysis scheme 300 of FIG. 3 in operating environment 400 of FIG. 4. Asshown in FIG. 5, historical data for an e-sport may be identified at502. For example, in operating environment 400 of FIG. 4, e-sportanalyzer 420 may identify historical data 412 provided by e-sportreporter 410. At 504, metadata for the e-sport may be identified. Forexample, in operating environment 400 of FIG. 4, e-sport analyzer 420may identify metadata 414 provided by e-sport reporter 410 and/ormetadata 427 provided by metadata extractor 426.

At 506, a decision scoring model for the e-sport may be trained usingthe historical data identified at 502 and the metadata identified at504. For example, in operating environment 400 of FIG. 4, e-sportanalyzer 420 may train a decision scoring model for an e-sport usinghistorical data 412, metadata 414, and/or metadata 427. At 508, decisionparameters may be determined for a gameplay decision of an ongoinggaming session of an e-sport. For example, in operating environment 400of FIG. 4, e-sport analyzer 420 may determine decision parameters 428.

At 510, a plurality of candidate in-game decision sequences may beidentified based on the decision parameters determined at 508 and agaming session history for the ongoing gaming session. For example, inoperating environment 400 of FIG. 4, e-sport analyzer 420 may identifycandidate decision sequences 429 based on decision parameters 428 and agaming session history comprised among session information 418. At 512,the decision scoring model may be applied to rank the plurality ofcandidate in-game decision sequences identified at 510. For example, inoperating environment 400 of FIG. 4, e-sport analyzer 420 may apply adecision scoring model to rank candidate decision sequences 429. Theembodiments are not limited to these examples.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 5, it isto be understood and appreciated that the claimed subject matter is notlimited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 6, a block diagram is shown illustrating anexample, non-limiting embodiment of a virtualized communication network600 in accordance with various aspects described herein. In particular avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of system 100, thesubsystems and functions of decision analysis scheme 300, and theoperations of method 500 presented in FIGS. 1, 3, and 5. For example,virtualized communication network 600 can facilitate in whole or in parttraining a decision scoring model for an e-sport based on historicaldata for the e-sport, determining decision parameters for a gameplaydecision of an ongoing gaming session of the e-sport, identifying aplurality of candidate in-game decision sequences based on the decisionparameters and a gaming session history for the ongoing gaming session,and applying the decision scoring model to rank the plurality ofcandidate in-game decision sequences.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 650, a virtualized network function cloud 625 and/or oneor more cloud computing environments 675. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 630, 632, 634, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router can be implemented via a VNE 630 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 650 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 630, 632 or 634. These network elements can be included intransport layer 650.

The virtualized network function cloud 625 interfaces with the transportlayer 650 to provide the VNEs 630, 632, 634, etc. to provide specificNFVs. In particular, the virtualized network function cloud 625leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 630, 632 and 634can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 630, 632 and 634 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 630, 632, 634, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 675 can interface with the virtualizednetwork function cloud 625 via APIs that expose functional capabilitiesof the VNEs 630, 632, 634, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 625. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 625 and cloud computingenvironment 675 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 7, there is illustrated a block diagram of acomputing environment 700 in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 7 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 700 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 700 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 630, 632,634, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 700 canfacilitate in whole or in part training a decision scoring model for ane-sport based on historical data for the e-sport, determining decisionparameters for a gameplay decision of an ongoing gaming session of thee-sport, identifying a plurality of candidate in-game decision sequencesbased on the decision parameters and a gaming session history for theongoing gaming session, and applying the decision scoring model to rankthe plurality of candidate in-game decision sequences.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM),flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 7, the example environment can comprise acomputer 702, the computer 702 comprising a processing unit 704, asystem memory 706 and a system bus 708. The system bus 708 couplessystem components including, but not limited to, the system memory 706to the processing unit 704. The processing unit 704 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 704.

The system bus 708 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 706comprises ROM 710 and RAM 712. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 702,such as during startup. The RAM 712 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 702 further comprises an internal hard disk drive (HDD) 714(e.g., EIDE, SATA), which internal HDD 714 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 716, (e.g., to read from or write to a removable diskette718) and an optical disk drive 720, (e.g., reading a CD-ROM disk 722 or,to read from or write to other high capacity optical media such as theDVD). The HDD 714, magnetic FDD 716 and optical disk drive 720 can beconnected to the system bus 708 by a hard disk drive interface 724, amagnetic disk drive interface 726 and an optical drive interface 728,respectively. The hard disk drive interface 724 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 702, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 712,comprising an operating system 730, one or more application programs732, other program modules 734 and program data 736. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 712. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 702 throughone or more wired/wireless input devices, e.g., a keyboard 738 and apointing device, such as a mouse 740. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 704 through aninput device interface 742 that can be coupled to the system bus 708,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 744 or other type of display device can be also connected tothe system bus 708 via an interface, such as a video adapter 746. Itwill also be appreciated that in alternative embodiments, a monitor 744can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 702 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 744, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 702 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 748. The remotecomputer(s) 748 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer702, although, for purposes of brevity, only a remote memory/storagedevice 750 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 752 and/orlarger networks, e.g., a wide area network (WAN) 754. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 702 can beconnected to the LAN 752 through a wired and/or wireless communicationnetwork interface or adapter 756. The adapter 756 can facilitate wiredor wireless communication to the LAN 752, which can also comprise awireless AP disposed thereon for communicating with the adapter 756.

When used in a WAN networking environment, the computer 702 can comprisea modem 758 or can be connected to a communications server on the WAN754 or has other means for establishing communications over the WAN 754,such as by way of the Internet. The modem 758, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 708 via the input device interface 742. In a networked environment,program modules depicted relative to the computer 702 or portionsthereof, can be stored in the remote memory/storage device 750. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 702 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 8, an embodiment 800 of a mobile network platform810 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 530, 532, 534, etc. For example, platform 810 can facilitatein whole or in part training a decision scoring model for an e-sportbased on historical data for the e-sport, determining decisionparameters for a gameplay decision of an ongoing gaming session of thee-sport, identifying a plurality of candidate in-game decision sequencesbased on the decision parameters and a gaming session history for theongoing gaming session, and applying the decision scoring model to rankthe plurality of candidate in-game decision sequences. In one or moreembodiments, the mobile network platform 810 can generate and receivesignals transmitted and received by base stations or access points suchas base station or access point 122. Generally, mobile network platform810 can comprise components, e.g., nodes, gateways, interfaces, servers,or disparate platforms, that facilitate both packet-switched (PS) (e.g.,internet protocol (IP), frame relay, asynchronous transfer mode (ATM))and circuit-switched (CS) traffic (e.g., voice and data), as well ascontrol generation for networked wireless telecommunication. As anon-limiting example, mobile network platform 810 can be included intelecommunications carrier networks, and can be considered carrier-sidecomponents as discussed elsewhere herein. Mobile network platform 810comprises CS gateway node(s) 812 which can interface CS traffic receivedfrom legacy networks like telephony network(s) 840 (e.g., publicswitched telephone network (PSTN), or public land mobile network (PLMN))or a signaling system #7 (SS7) network 860. CS gateway node(s) 812 canauthorize and authenticate traffic (e.g., voice) arising from suchnetworks. Additionally, CS gateway node(s) 812 can access mobility, orroaming, data generated through SS7 network 860; for instance, mobilitydata stored in a visited location register (VLR), which can reside inmemory 830. Moreover, CS gateway node(s) 812 interfaces CS-based trafficand signaling and PS gateway node(s) 818. As an example, in a 3GPP UMTSnetwork, CS gateway node(s) 812 can be realized at least in part ingateway GPRS support node(s) (GGSN). It should be appreciated thatfunctionality and specific operation of CS gateway node(s) 812, PSgateway node(s) 818, and serving node(s) 816, is provided and dictatedby radio technology(ies) utilized by mobile network platform 810 fortelecommunication over a radio access network 820 with other devices,such as a radiotelephone 875.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 818 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 810, like wide area network(s) (WANs) 850,enterprise network(s) 870, and service network(s) 880, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 810 through PS gateway node(s) 818. It is to benoted that WANs 850 and enterprise network(s) 870 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 820, PS gateway node(s) 818 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 818 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 800, mobile network platform 810 also comprises servingnode(s) 816 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 820, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 818. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 818; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 816 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)814 in mobile network platform 810 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 810. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 818 for authorization/authentication and initiation of a datasession, and to serving node(s) 816 for communication thereafter. Inaddition to application server, server(s) 814 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 810 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 812and PS gateway node(s) 818 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 850 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 810 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 814 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 810. To that end, the one or more processor can executecode instructions stored in memory 830, for example. It is should beappreciated that server(s) 814 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 800, memory 830 can store information related tooperation of mobile network platform 810. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 810, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 830 can also storeinformation from at least one of telephony network(s) 840, WAN 850, SS7network 860, or enterprise network(s) 870. In an aspect, memory 830 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 8, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 9, an illustrative embodiment of a communicationdevice 900 is shown. The communication device 900 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 900 can facilitate in whole or in part training adecision scoring model for an e-sport based on historical data for thee-sport, determining decision parameters for a gameplay decision of anongoing gaming session of the e-sport, identifying a plurality ofcandidate in-game decision sequences based on the decision parametersand a gaming session history for the ongoing gaming session, andapplying the decision scoring model to rank the plurality of candidatein-game decision sequences.

The communication device 900 can comprise a wireline and/or wirelesstransceiver 902 (herein transceiver 902), a user interface (UI) 904, apower supply 914, a location receiver 916, a motion sensor 918, anorientation sensor 920, and a controller 906 for managing operationsthereof. The transceiver 902 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 902 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 904 can include a depressible or touch-sensitive keypad 908 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device900. The keypad 908 can be an integral part of a housing assembly of thecommunication device 900 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 908 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 904 can further include a display910 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 900. In anembodiment where the display 910 is touch-sensitive, a portion or all ofthe keypad 908 can be presented by way of the display 910 withnavigation features.

The display 910 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 900 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 910 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 910 can be an integral part of the housingassembly of the communication device 900 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 904 can also include an audio system 912 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 912 can further include amicrophone for receiving audible signals of an end user. The audiosystem 912 can also be used for voice recognition applications. The UI904 can further include an image sensor 913 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 914 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 900 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 916 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 900 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 918can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 900 in three-dimensional space. Theorientation sensor 920 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device900 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 900 can use the transceiver 902 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 906 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 900.

Other components not shown in FIG. 9 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 900 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying acquired cell sites that provide a maximumvalue/benefit after addition to an existing communication network) canemploy various AI-based schemes for carrying out various embodimentsthereof. Moreover, the classifier can be employed to determine a rankingor priority of each cell site of the acquired network. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, . . . ,xn), to a confidence that the input belongs to a class, that is,f(x)=confidence (class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approaches canalso be employed; e.g., naïve Bayes, Bayesian networks, decision trees,neural networks, fuzzy logic models, and probabilistic classificationmodels. Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device, comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, the operations comprising: assembling historical data for anelectronic sport (e-sport); training a decision scoring model for thee-sport using the historical data for the e-sport; determining decisionparameters for a gameplay decision of an ongoing gaming session of thee-sport; based on the decision parameters and a gaming session historyfor the ongoing gaming session, identifying a plurality of candidatein-game decision sequences; and applying the decision scoring model torank the plurality of candidate in-game decision sequences.
 2. Thedevice of claim 1, wherein the applying the decision scoring model torank the plurality of candidate in-game decision sequences includesdetermining a respective aggregate score for each candidate in-gamedecision sequence of the plurality of candidate in-game decisionsequences.
 3. The device of claim 2, wherein the respective aggregatescore for each candidate in-game decision sequence of the plurality ofcandidate in-game decision sequences is determined based on respectivescores for a plurality of decisions within that candidate in-gamedecision sequence.
 4. The device of claim 1, wherein the historical datafor the e-sport includes one or both of: aggregated player-levelstatistics associated with one or more prior gaming sessions of thee-sport; and aggregated team-level statistics associated with one ormore prior gaming sessions of the e-sport.
 5. The device of claim 1,wherein the operations further comprise training the decision scoringmodel for the e-sport using the historical data for the e-sport andmetadata associated with the e-sport.
 6. The device of claim 5, whereinthe operations further comprise using a metadata extraction model toobtain at least a portion of the metadata associated with the e-sport byanalyzing one or more video feeds, one or more audio feeds, or acombination of both.
 7. The device of claim 1, wherein the identifyingthe plurality of candidate in-game decision sequences includessimulating one or more in-game scenarios based on an assumed decision.8. The device of claim 1, wherein each candidate in-game decisionsequence of the plurality of candidate in-game decision sequencescomprises a potential sequence of decisions occurring subsequent to asame point in time.
 9. A non-transitory machine-readable medium,comprising executable instructions that, when executed by a processingsystem including a processor, facilitate performance of operations, theoperations comprising: identifying historical data for an electronicsport (e-sport) and metadata for the e-sport; training a decisionscoring model for the e-sport based on the historical data for thee-sport and the metadata for the e-sport; determining decisionparameters for a gameplay decision of an ongoing gaming session of thee-sport; based on the decision parameters and a gaming session historyfor the ongoing gaming session, identifying a plurality of candidatein-game decision sequences; and applying the decision scoring model torank the plurality of candidate in-game decision sequences.
 10. Thenon-transitory machine-readable medium of claim 9, wherein theoperations further comprise extracting at least a portion of themetadata for the e-sport from a video feed.
 11. The non-transitorymachine-readable medium of claim 9, wherein the operations furthercomprise extracting at least a portion of the metadata associated withthe e-sport from an audio feed.
 12. The non-transitory machine-readablemedium of claim 9, wherein the applying the decision scoring model torank the plurality of candidate in-game decision sequences includesdetermining a respective aggregate score for each candidate in-gamedecision sequence of the plurality of candidate in-game decisionsequences.
 13. The non-transitory machine-readable medium of claim 12,wherein the respective aggregate score for each candidate in-gamedecision sequence of the plurality of candidate in-game decisionsequences is determined based on respective scores for a plurality ofdecisions within that candidate in-game decision sequence.
 14. Thenon-transitory machine-readable medium of claim 9, wherein thehistorical data for the e-sport includes aggregated player-levelstatistics associated with one or more prior gaming sessions of thee-sport.
 15. The non-transitory machine-readable medium of claim 9,wherein the historical data for the e-sport includes aggregatedteam-level statistics associated with one or more prior gaming sessionsof the e-sport.
 16. A method, comprising: assembling, by a processingsystem including a processor, historical data for an electronic sport(e-sport); obtaining, by the processing system, metadata for thee-sport; training, by the processing system, a decision scoring modelfor the e-sport based on the historical data for the e-sport and themetadata for the e-sport; determining, by the processing system,decision parameters for a gameplay decision of an ongoing gaming sessionof the e-sport; based on the decision parameters and a gaming sessionhistory for the ongoing gaming session, identifying, by the processingsystem, a plurality of candidate in-game decision sequences; andapplying, by the processing system, the decision scoring model to rankthe plurality of candidate in-game decision sequences.
 17. The method ofclaim 16, further comprising extracting, by the processing system, atleast a portion of the metadata for the e-sport from a video feed. 18.The method of claim 16, further comprising extracting, by the processingsystem, at least a portion of the metadata for the e-sport from an audiofeed.
 19. The method of claim 16, wherein the applying the decisionscoring model to rank the plurality of candidate in-game decisionsequences includes determining a respective aggregate score for eachcandidate in-game decision sequence of the plurality of candidatein-game decision sequences.
 20. The method of claim 19, wherein therespective aggregate score for each candidate in-game decision sequenceof the plurality of candidate in-game decision sequences is determinedbased on respective scores for a plurality of decisions within thatcandidate in-game decision sequence.